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1. Field of the Invention
The invention relates to imaging systems for detecting contamination on foods. The imaging systems can be used, for example, for real-time detection of fecal and ingesta on meat and poultry carcasses which may be present when carcasses are being processed. The systems include both hyperspectral and multispectral imaging systems including apparatus, methods, and computer readable mediums.
2. Description of the Related Art
Microbial pathogens in food cause an estimated 76 million cases of human illnesses and up to 5,000 deaths annually, according to the Center for Disease Control and Prevention (Mead et al. Emerging Infectious Diseases 5(5) 607-625, 1999). In 1996, the USDA Economic Research Service reported that the annual cost of the food-borne illnesses caused by six common bacterial pathogens: Campylobacter spp., Clostridium perfringens, Escherichia coli O157:H7, Listeria monocytogenes, Salmonella spp., and Staphylococcus aureus; ranges from 2.9 billion to 6.7 billion dollars. The foods most likely to cause these illnesses are animal products such as red meat, poultry and eggs, seafood, and dairy products.
Contamination of meat and poultry in particular, with many bacterial food-borne pathogens, can occur as a result of exposure of the animal carcass to ingesta and/or fecal material during or after slaughter. Accordingly, in order to minimize the likelihood of such contamination, it has been necessary to examine each food item individually to detect the presence of contaminants. Historically, such inspection has been performed visually by U.S.D.A. inspectors, who examine each individual food item as it passes through the processing system.
With poultry, for example, in a modern poultry processing plant, carcasses are placed on shackles of a processing line conveyor system for dressing and inspection. Typically, such conveyors operate at speeds of up to 140 carcasses per minute, with a six inch separation between shackles holding carcasses. Even with multiple inspectors continuously performing such inspection, as little as two seconds are allotted for the inspection of each carcass.
During this inspection period, the inspector is required to check for evidence of eight different diseases as well as for certain quality characteristics, to verify that the chicken was alive when placed on the production line, and to check for evidence of ingesta or fecal contamination. Moreover, during a typical business day operating in two eight hour shifts, a productive poultry processing plant may produce as many as 250,000 processed chickens.
After slaughter, each carcass is examined for disease or evidence of contamination that would render all or part of the carcass unfit for human consumption. Currently the meat processing industry relies upon a variety of methods for the inspection of animal carcasses. These methods typically include human visual inspection, microbiological culture analysis, bioluminescent ATP-based assays, and antibody-based microbiological tests. Unfortunately, these procedures are labor intensive, time consuming, and do not meet the needs of the meat processing industry for an accurate high speed, non-invasive method that is amenable to inspection and real-time analysis.
It is apparent from this brief description that the historical inspection of meat carcases by human inspectors is problematic, and that it is poorly suited to the effective detection and elimination of contaminants in modern poultry processing plants. In particular, it requires the inspectors to make a subjective determination repeatedly. Such a system is prone to errors, which can lead to the entry of contaminated poultry products into the commercial distribution system.
In 1994, the Food Safety Inspection Service (FSIS) published a proposed rule, xe2x80x9cEnhanced Poultry Inspectionxe2x80x9d (USDA, Proposed Rule, Fed. Reg. Volume 59, 35659, 1994) to clarify and strengthen the FSIS""s zero-tolerance policy for visible fecal contamination on poultry carcasses. Prior to this rule, FSIS ensured removal of all visible fecal contamination subsequent to postmortem inspection through off-line reinspection, direct on-line observations by an inspector, and application of finished product standards (FPS). Any bird found to be contaminated with feces was set aside for rework or condemnation. The proposed Enhanced Poultry Inspection rule removed xe2x80x9cfecesxe2x80x9d from the list of defects in the FPS.
Since the proposed rule was published, FSIS has adopted the Pathogen Reduction; Hazard Analysis and Critical Control Points (HACCP) Systems (USDA, Final Rule, Fed. Reg., Volume 61, 28805-38855, 1996). The Pathogen Reduction/HACCP system superceded the provisions of the Enhanced Poultry Inspection rule. However, FSIS determined that the zero fecal tolerance provision would complement the Pathogen Reduction/HACCP regulations. Therefore, FSIS finalized the zero fecal tolerance provision of the Enhanced Poultry Inspection proposal (USDA, Final Rule, Fed. Reg., Volume 62, 5139-5143, 1997).
The HACCP regulations require meat processing establishments to identify all food safety hazards likely to occur in a specific process, and to identify critical control points adequate to prevent them. Zero tolerance for visible fecal contamination is a standard that has been implemented by FSIS, forcing poultry processing plants to adopt some point in the evisceration process as a critical control point under HACCP regulations which can be achieved by control, and therefore, is consistent with the HACCP framework. If evisceration machinery is not adjusted properly, the digestive tract of the bird may be torn during evisceration and its contents may leak onto the carcass. In meat processing establishments, fecal contamination of carcasses is a food safety hazard because of its link to microbiological contamination and food borne illness (USDA, 1997, supra). Pathogens may reside in fecal material and ingesta, both within the gastrointestinal tract and on the exterior surface of animals going to slaughter. Therefore, without proper procedures during slaughter and processing, the edible portions of the carcass can become contaminated with bacteria capable of causing illness in humans. Preventing carcasses with visible fecal and ingesta contamination from entering the chlorinated ice water bath (chiller) is critical for preventing cross-contamination of other carcasses. Thus, the final carcass wash, before entering the chiller, has been adopted by many poultry processors as a HACCP system critical control point for preventing cross-contamination of other carcasses.
Compliance with zero tolerance in meat processing establishments is currently verified by visual observation. Three criteria are used for identifying fecal contamination (USDA, 1997, supra). These are color, consistency, and composition. In general, fecal material color ranges from varying shades of yellow to green, brown and white; the consistency of feces is usually semi-solid to paste; and the composition of feces may include plant material. Inspectors use these guidelines to verify that establishments prevent carcasses with visible fecal contamination from entering the chillers. me Visual inspection is both labor intensive and prone to both human error and variability. In addition, there has been a dramatic increase in water usage in most plants as a result of the zero-tolerance fecal standard. Plants have nearly doubled their previous water usage and nationwide the usage has increased an estimated 2 billion gallons (Jones, Poultry, Volume 6, 38-41, 1999).
Efforts have been made to develop automated or semiautomated visual inspection systems for detecting the presence of contaminants on food products during processing. Most systems utilize a technique in which the food item is irradiated with light having a frequency, for example, in the UV range, such that it causes the emission of fluorescent radiation upon striking fecal matter or ingesta. Fluorescent light emanating from the target food item is then measured and compared with a threshold value. If the light gathered exceeds the threshold, a signal indicative of the presence of fecal contamination or ingesta is generated. Such a system is disclosed for example in U.S. Pat. Nos. 5,621,215 and 5,895,921 to Waldroup et al., and U.S. Pat. No. 5,821,546 to Xiao et al.
U.S. Pat. No. 5,914,247 to Casey et al. discloses a fecal and ingesta contamination detection system which is based on the premise that the emission of fluorescent light having a wavelength between about 660 and 680 nm is indicative of the presence of ingesta or fecal material. Thus, carcases being processed are illuminated with UV or visible light (suitable wavelengths being between 300 and 600 nm) and the illuminated surface is then examined for the emission of fluorescent light in the 660 and 680 range. In a preferred embodiment, the intensity of such fluorescence in the 660-680 nm range is compared with that in the 600-620 range as a baseline in order to distinguish fluorescent light emissions of the carcasses themselves.
Visible and near-infrared reflectance (Vis/NIR) spectroscopy is a technique that can be used to detect contamination on foodstuffs. It is a nonconsumptive, instrumental method for fast, accurate, and precise evaluation of the chemical composition of agricultural materials (Williams, Commercial near-infrared reflectance analyzers. In Williams and Norris, eds., Near Infrared Technology in the Agricultural and Food Industries, Am. Assoc. Cereal Chem., St. Paul, Minn., 1987, pp. 107-142). The use of Vis/NIR spectroscopic techniques for classifying wholesome, septicemic, and cadaver carcasses have been reported by Chen and Massie (ASAE, Volume 36(3), 863-889, 1993) and Chen et al. (Appl. Spectrosc., Volume 50, 910-916, 1996b). These studies were conducted with a near-infrared reflectance (NIR) probe in contact with a stationary carcass. More recently, Chen and Hruschka (ASAE Paper No. 983047, American Society of Agricultural Engineers, St. Joseph, Mich., 1999) disclosed an on-line transportable Vis/NIR system (400 to 1700 nm) in which the probe was not in contact with the carcass and carcasses were moving at rates of either 60 or 90 birds per minute. Carcasses were classified as wholesome or unwholesome with an average accuracy of 94% and 97.5% when measured in room light and in the dark, respectively. On-line trials were conducted in a slaughter establishment where spectra of normal and abnormal carcasses were measured. The Vis/NIR system measured carcasses at a rate of 70 birds per minute and was able to classify the carcasses from the spectral data with a success rate of 95% (Chen and Hruschka, 1998, supra). The Vis/NIR method showed promise for separation of wholesome and unwholesome carcasses in a partially automated system. The use of the technique to detect fecal and ingesta surface contaminants on poultry carcasses has not been attempted in the processing plant.
Machine vision is a technology for automating production processes with vision capabilities. Even though machine vision has evolved into a promising technology for many agricultural product applications, such as grading or inspection, there are many factors to be considered in on-line applications: processing speed, reliability, and applicability for industrial environments (Sakar and Wolfe, Trans. ASAE, Volume 28(3), 970-979, 1985; Miller and Delwiche, Trans. ASAE, Volume 32(4), 1484-1490, 1989; Tao et al., Trans. ASAE Volume 38(5), 1555-1561, 1995; Steinmetz et al., Trans. ASAE, Volume 37(4), 1347-1353, 1994; Ni et al., ASAE Paper No. 933032, American Society of Agricultural Engineers, St. Joseph, Mich., 1993; Daley et al., Proc. SPIE, Volume 2345, 403-411, 1994). Image processing techniques have made machine vision research possible to identify and classify agricultural commodities in the spatial domain (Guyer et al., Trans. ASAE, Volume 29(6), 863-869, 1986) as well as in the spectral domain (Meyer et al., Applied Engineering in Agriculture, Volume 8(5), 715-722, 1992).
Machine vision techniques are feasible for grading and parts identification in poultry production (Daley et al., Proceedings of Robotics and Vision ""88, Society of Manufacturing Engineers, Dearborn, Mich., 1988). Techniques for recognizing global or systemic defects on poultry carcasses with a color imaging system were reported by Daley et al. (1994, supra ) and Chin et al. (Experimental evaluation of neural networks for inspection of chickens. Research Report of Georgia Tech. Research Institute, 1993). However, this approach had a 90% accuracy for global defect classification and only a 60% accuracy for local defect classification (Daley and Carey, Color machine vision for defect detection: Algorithms and techniques, RIA International Robots and Vision Conf., 1991). Even though a color imaging system has the ability to extract the salient image features, this system was not successful for totally automated inspection because of low accuracy (Daley, Color machine vision for industrial inspection advances and potential for the future, Research Report of Georgia Tech. Research Institute, 1992).
Multispectral imaging technology has potential for food inspection application. Since biological materials at different conditions have different spectral reflectance characteristics, the status of materials could be identified based on their spectral images by selecting optimum wavelengths. Several spectral image processing algorithms have been developed to differentiate wholesome carcasses from unwholesome carcasses (Park and Chen, ASAE Paper No. 946027, American Society of Agricultural Engineers, St. Joseph, Mich., 1994a; Park et al., Trans. ASAE, Volume 39(5), 1933-1941, 1996a). Use of intensities, recorded in different spectral bands of a multispectral camera for segmentation, was effective for classification of poultry carcasses (Park and Chen, Trans. ASAE, Volume 37(6), 1983-1988, 1994b; Park et al., 1996a, supra). Multispectral imaging was used for detecting unwholesome conditions, such as septicemia, cadaver, bruise, tumor, air-sacculitis, and ascites, in poultry carcasses (Park et al., 1996a, supra). Park and Chen (1994b, supra) developed a prototype multispectral imaging system for detecting abnormal poultry carcasses, specifically, to determine the optimal wavelengths of multispectral filters for discerning septicemic and cadaver carcasses from normal carcasses, and to develop a discriminate function for separation of the abnormal carcasses with an accuracy of 93% for normal, 83% for septicemic, and 97% for cadaver carcasses.
Textural feature analysis of multispectral images has potential to discriminate wholesome carcasses from septicemic and cadaver carcasses with high classification accuracy of about 94% (Park and Chen, Trans. ASAE, Volume 39(4), 1485-1491, 1996). However, texture feature analysis would not be useful for an on-line system because of heavy computing time. To achieve real-time processing and analyzing of multispectral gray-scale images for on-line separation of septicemic, cadaver, tumorous, bruised, and other damaged carcasses from the wholesome carcasses, a neural network algorithm was found to be useful (Park et al., ASAE Paper No. 983070, American Society of Agricultural Engineers, St. Joseph, Mich., 1998b). Thus, image texture analysis is an important process in scene analysis because it partitions an image into meaningful regions. Lumia et al., (Pattern Recognition, Volume 16(1), 39-46,1983) described a method for discriminating texture classes based on the measurements of small regions determined by an initial segmentation of the image for categorizing homogeneous regions. Park and Chen (1996, supra) have reported that textural feature analysis of multispectral images containing Vis/NIR wavelengths based on co-occurrence matrices was feasible for discriminating abnormal from normal poultry carcasses at 542 nm.
Development of high speed and reliable inspection systems to ensure safe production of poultry processing has become an important issue. Two dual-wavelength vision systems were developed for on-line machine vision inspection of poultry carcasses (Chao et al., ASAE Paper No. 993118, American Society of Agricultural Engineers, St. Joseph, Mich., 1999). A real-time multispectral image processing algorithm was developed from neural network models with different learning rules and transfer functions for on-line poultry carcass inspection (Park et al., Journal of Agricultural Engineering Research, Volume 69, 351-363, 1998c). The classification accuracy with dual-wavelength spectral images was much higher than single wavelength spectral images in identifying unwholesome poultry carcasses (Chao et al., 1999, supra). Object-oriented software was developed for on-line image capture, off-line development of classification models, and on-line prediction of wholesome and unwholesome carcasses.
An extension of multispectral imaging is known as hyperspectral imaging which is also referred to as imaging spectrometry. Whereas multispectral imaging consists of measurements from two to about ten discrete wavelengths for a given image, hyperspectral imaging measures more than ten contiguous wavelengths, often many more. Like multispectral imaging, hyperspectral imaging is an imaging technique that combines aspects of conventional imaging with spectrometry and radiometry. The result is a technique that is capable of providing an absolute radiometric measurement over a contiguous spectral range for each and every pixel of an image. Thus, data from a hyperspectral image contains two-dimensional spatial information plus spectral information over the spatial image. These data can be considered as a three-dimensional hypercube which can provide physical and geometric observations of size, dimension, orientation, shape, color, and texture, as well as chemical/molecular information such as water, fat, proteins, and other hydrogen-bonded constituent as described above in other Vis/NIR research. Hyperspectral imaging is often used in remote sensing applications (Schowengerdt, The nature of remote sensing, In Remote sensing: Models and methods for image processing, San Diego, Academic Press, 1997, pp. 1-33), but is also being utilized in medical, biological, agricultural, and industrial areas as well (Lu and Chen, SPIE, Volume 3544, 121-133, 1998; Heitschmidt et al., SPIE, Volume 3544, 134-137, 1998; Levenson et al., SPIE, Volume 3438, 300-312, 1998; Lu et al., ASAE Paper No.993120, American Society of Agricultural Engineers, St. Joseph, Mich., 1999; Willoughby et al., SPIE, Volume 2599, 264-272, 1996).
Since the detectors used to measure hyperspectral data are two-dimensional focal plane arrays (FPA), while hyperspectral data are three-dimensional, there must be a technique to collect all the data. The two primary techniques for collecting hyperspectral images are collecting two-dimensional spatial images while sequentially varying a narrow bandwidth of incident energy, or collecting full spectral information of a line-scan image while sequentially varying the position of the line scan (Wolfe, Introduction to imaging spectrometers, SPIE Optical Engineering Press, Bellingham, Wash., 1997; Fisher et al., SPIE, Volume 3438, 23-30, 1998; Hart and Slough, SPIE, Volume 3389, 139-149, 1998). The first technique can typically be demonstrated with either an acousto-optic tunable filter (AOTF) or a liquid-crystal tunable filter (LCTF) in front of a FPA where a two-dimensional spatial image is captured at successive wavelengths. The latter technique is usually implemented in remote sensing as either a push-broom or whisk-broom scanner where a line-scan spectrometer is positioned in front of the FPA so that the FPA successively captures one spatial dimension and one spectral dimension as the scanner or image travels normal to the line-scan direction (first spatial dimension). With each technique, the successive images must be combined to build a hypercube of data for a given image. Each technique has advantages and disadvantages that dictate their use in varying applications. LCTF and AOTF systems can rapidly collect images at discrete wavelengths, which can be easily varied. However, they are better suited for stationary objects to avoid image shifting between discrete wavelength measurements. Push-broom and whisk-broom systems are better suited for moving objects but cannot measure at discrete wavelengths.
Hyperspectral imaging has recently been used to explore the feasibility of detecting defects and contaminants in poultry carcasses (Lu and Chen, 1998, supra; Heitschmidt et al., 1998, supra). Lu et al. (1999, supra) demonstrated that taking a second-derivative of the reflectance value could qualitatively distinguish between four normal carcasses and four cadaver, four septicemia, and three tumorous carcasses. Heitschmidt et al. (1998, supra) contaminated two carcasses with fecal material and were able to qualitatively identify the contaminants with principal component analysis (PCA). However, the time required to perform the PCA was over 40 minutes for a single carcass. Image ratios (wavelength ratios) were also examined. No specific wavelengths were identified as significant for detecting fecal contamination with the limited sample population.
Hyperspectral imaging is an extremely useful tool to throughly analyze the spectra of inhomogeneous materials that contain a wide range of spectral information. It can be an effective technique for identifying surface contaminant on poultry carcasses. At the current time though, it is not suitable for on-line identification of fecal contamination because of lengthy image acquisition and processing times.
It is therefore, an object of the present invention to provide imaging systems and methods for detecting contamination on foods.
Another object of the present invention is to provide improved processes and apparatus for detection of contamination on a food item, which achieves enhanced accuracy and dependability in positively identifying contaminants.
Another object of the present invention is to provide processes and apparatus which can reliably detect contaminants at a speed which is compatible with the rate at which a food is processed on a production line.
A still further object of the present invention is to provide real-time automated food inspection systems which can quickly and accurately identify contaminated food items in a food processing line.
This and other objects and advantages are achieved by the imaging systems according to the invention, in which digital imaging sensors, such as multispectral or hyperspectral imaging camera units are used to collect reflectance data from a food source on which contamination is to be detected. Reflectance data gathered by the imaging system are then processed in a digital computer using specially derived algorithms for enhancing the detection of contamination.
The theoretical development of algorithms which are used for this purpose is based on the difference between spectral reflectance of contaminants versus that of uncontaminated food. The assumption is made that a mathematical combination of remotely sensed spectral bands could be used to identify contaminants. The results generated by such a combination of spectral bands corresponds to the amount of contaminants in a given image pixel.
There are two categories of algorithms that have been developed for use in the detection of contaminants. The first is a ratio of key wavelengths or bands that are determined. The purpose behind using a ratio is to alter the reflectance measurements of spectral bands using an illumination independent function, which will augment the spectral values for the contaminant while diminishing the values for the food source or background.
Examples range from a simple ratio of two wavelength images, to a ratio of multiple wavelength image combinations, such as   (                              (                                    λ              1                        +            x                    )                2            ⁢              (                              λ            2                    -                      λ            4                          )                            (                              λ            1                    +                      λ            3                          )            ⁢              xe2x80x83            ⁢              (                  λ          2                )              )
where xcex1, xcex2, xcex3, and xcex4 are images at four key wavelengths, and x is a constant. Another example in the ratio category would be the well-known normalized difference vegetative index (NDVI).
The second category of algorithm is defined as a linear combinations of wavelengths. The linear combinations category can range from a combination of two wavelengths (xcex1+xcex2), to a linear combination of wavelength ratios, such as:       (                            λ          1                +                  λ          2                -        w                              λ          2                -        x              )    +      (                            λ          1                +                  λ          3                -        w                              λ          3                -        x              )    -      (                            λ          1                +                  λ          4                -        y                              λ          4                -        z              )  
where xcex1, xcex2, xcex3, and xcex4 are images at four key wavelengths, and w, x, y, and z are constants. This category also includes previously published remote sensing algorithms such as the Mahalonobis Distance and the rule file generation of the Spectral Angle Mapper. These formulas may need to be combined with a known filter for optimum results. Once an equation has been used, it may be necessary to apply any of a number of imaging filters to the resultant data set, either for clarity, to sharpen results, or even to limit the error. Some examples of these are low pass, high pass, median, gaussian, laplacian and texture filters.
Further objects and advantages of the invention will become apparent from the following description.